CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer
This provides a solution for robotics and computer vision applications, but it is incremental as it applies a transformer-based architecture to an existing task.
The paper tackles the problem of estimating joint parameters and states for articulated objects from a single point cloud, achieving high precision and robustness with experimental results showing it outperforms existing alternatives on several category datasets.
The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimation from a point cloud using Transformer. CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point cloud. The proposed CAPT methods accurately estimate joint parameters and states for various articulated objects with high precision and robustness. The paper also introduces a motion loss approach, which improves articulation estimation performance by emphasizing the dynamic features of articulated objects. Additionally, the paper presents a double voting strategy to provide the framework with coarse-to-fine parameter estimation. Experimental results on several category datasets demonstrate that our methods outperform existing alternatives for articulation estimation. Our research provides a promising solution for applying Transformer-based architectures in articulated object analysis.